Modern suites of well logging measurements are used to predict porosity and fluid saturations of reservoir rocks surrounding a borehole. Porosity and fluid saturations are useful for accurate reserve estimation and identification of potential pay zones. More accurate porosities and fluid saturations may be predicted if detailed and accurate mineralogical information is available. Mineralogical data provide more accurate characterization of logging tool responses and, as a result, lead to improved log interpretations. Knowledge of the clay mineral types present in reservoir rocks and their volumes is an indicator of reservoir quality and is also used in the selection of hydraulic fracturing, completion, and stimulation fluids.
Spectral gamma-ray logging tools provide elemental compositions of reservoir rocks (e.g., Si, Al, Ca, Mg, K, Fe, S, etc.) derived from capture and inelastic neutron gamma ray spectroscopy. The elemental compositions are given as the weight fractions of the individual elements present in the rock matrix. They are used to predict mineralogy and rock properties such as grain density. The inversion of elemental composition to predict accurate mineralogy is a complex issue in reservoir characterization. The complexity arises because of the large number of minerals that are commonly found in reservoir rocks and the variability of the compositions of these minerals. Moreover, the mineralogy inverse problem may be complicated by the fact that many of the measured elements are common to different minerals. Thus, there exists a degree of non-uniqueness in the reconstruction of mineralogy from elemental composition data.
In the absence of gamma ray spectroscopy data, petrophysicists and other log analysts use conventional logging tool measurements to define the reservoir lithology or mineralogy. The different responses of neutron, density, and sonic logs in sandstones, limestones, and dolomites are cross-plotted in logging service company chart books and can be used to identify lithology and correct the log derived porosities. Shale volumes are predicted from natural gamma-ray logs, neutron-density, dielectric, and other logging tool responses. These shale volume estimates may depend on choosing a “clean sand” tool response in the well. The sands selected as clean can in fact be quite shaly, and this leads to unknown errors in the estimated shale volumes. Without the elemental composition data provided by gamma ray spectroscopy tools, it may not be possible to derive a detailed and accurate description of the reservoir mineralogy.
Because of the complexity of the mathematical relationship between elemental composition and mineralogy it is difficult to derive accurate forward models that predict mineralogy from rock chemistry. This is also true for most other reservoir characterization issues for which idealized forward models do not accurately account for the behavior of complex reservoir rocks and fluids.